nearby clusters. The upper left panel of Fig. 5.7 shows our fits to the full sample compared to those of Corteseet al. (2006) and the fit parameters are summarised in Table 5.2.
The upper right panel of Fig. 5.7 shows our non-linear fit to the same data compared to those of Konget al. (2004) and Boissier et al. (2007). Our fit is closer to that of Kong et al. but shifted to higher values ofβ and with smaller slope, Konget al. use a sample of 50 starburst galaxies, while Boissier et al. take a sample of 43 nearby, late-type galaxies.
With the exception of the interacting M51, it can be argued that all the galaxies in our sample are normal quiescent star forming systems. However, because we consider emission on pixel scales (rather than on a galaxy-wide scale), each pixel ought to be considered as a HIIregion, in which case we would expect the behaviour to be closer to the starburst sample fitted by Kong et al. , as is seen in Fig. 5.7. Table 5.2 gives the parameters of each non-linear fit.
We use the Akaike Information Criterion (AIC; Akaike 1974) to choose the best fit between the linear and non-linear cases, defined as
AIC =−2ln(L) + 2K (5.21) where ln(L) is the logarithm of maximized likelihood function, and K is the number of parameters in the model. In the case of least squares regression for normally distributed errors, (as it is the case here), it can be computed as
AIC =N log(ˆσ2) + 2K (5.22) whereσˆ2 is the variance and N is the sample size. The ‘best’ model is that with the lowest AIC among a set of specified models, and which best describes the data with the fewest number of free parameters.
Applying equation 5.22 to the fits from equations 5.19 and 5.20 reveal the non-linear relation to be the better fit of the two.
The pixel points in the upper left panel of Fig. 5.7 are colour-coded by galaxy and highlight the different regions occupied by each galaxy in the IRX−β plane. The points of the irregular galaxy IC 2574 are confined to lower IRX and β values in contrast with the other spiral galaxies. We note that M51 exhibits the greatest scatter about its IRX−β fit
compared to the other spirals.
The lower panels of Fig. 5.7 show the curves of best fit for each galaxy compared to the overall fit for the sample, in both the linear (left panel) and non-linear (right panel) cases. While the non-linear case is a better fit to the sample as a whole, it would appear to be inappropriate in the case of individual galaxies. Notwithstanding our small sample size, the upper left panel of Fig. 5.7 suggest that different morphologies are reflected in varying ranges for IRX and β, and would explain the non-linear nature of the full sample.
We would conclude that for a sufficiently restricted mix of morphological types and galaxy mass, the IRX−β relation is linear, and that care should be exercised when fitting a sample that is not.
Table 5.3 lists the fit parameters to individual galaxies for both the linear and non-linear case.
Figure 5.7: IRX−β plots where each galaxy is represented by a different colour and symbol, as specified in the key. upper left: Linear fit for all points combined (red line) compared to that of Corteseet al. (2006) (black line). Also shown are the OLS (Ordinary Least Squares) bisectors OLS(Y|X) and OLS(X|Y) from which our full fit was derived. upper right: Non- linear fit for all points combined (red line) compared to those of Konget al. (2004) (dashed line) and Boissieret al. (2007) (dot-dash line). In this case we have plotted our full sample as a density distribution with log contours. Also shown (blue line) is the linear fit from the upper left panel. lower left: Linear fits of the IRX−β relation for individual galaxies, using identical colours and symbols to those in the upper left panel. lower right: Non-linear fits per galaxy showing the same colours and symbols as the other panels.
Table5.2:IRX−βfitsforthefullsample. FitThispaperLiterature OLS(Y|X)log(IRX)=0.459(±0.002)β+1.001(±0.002),R=0.662 LinearfitsOLS(X|Y)β=1.033(±0.006)log(IRX)−1.387(±0.005),R=0.662log(IRX)=0.7(±0.06)β+1.30(±0.06)(a OLSbisectorlog(IRX)=0.662(±0.004)β+1.151(±0.004) Non-linearfitIRX=101.225(±0.009)+0.239(±0.004)β −3.736(±0.311),R=0.589IRX=102.10+0.85β −0.95(b) IRX=101.145+0.324β −3.136(c) (a) Corteseetal.2006 (b)Kongetal.2004 (c) AdaptedfromBoissieretal.2007;ThefitgivenisIRX=100.561+0.713(FUV−NUV) −3.136,whereFUV−NUVisthecolor betweenthetwoGALEXbands.Thedirectrelationbetweenβandthe(FUV-NUV)isgivenbyequation5.18. Table5.3:IRX−βbygalaxy. Linearfit:log(IRX)=˜aβ+˜bNon-linearfit:IRX=10a+bβ −c ˜a˜bRabcR M510.575±0.0071.155±0.0100.4931.292±0.0310.169±0.0136.245±1.360.412 M630.409±0.0081.191±0.0090.6591.107±0.0270.288±0.014-3.687±0.720.639 M740.623±0.0151.090±0.0140.3570.631±0.0780.223±0.054-1.739±0.750.276 M940.564±0.0091.156±0.0110.6731.241±0.0330.185±0.0165.79±1.280.672 M1000.590±0.0111.070±0.0010.6741.099±0.0230.305±0.0141.518±0.590.693 IC25740.835±0.0161.042±0.0430.440.231±0.0260.245±0.0540.112±0.160.359
General Conclusions and Future perspectives.
”El ignorante afirma, el sabio duda y reflexiona.’
Arist´oteles.
6.1 Summary
The scientific purpose for this thesis work is the stellar formation processes at different scales. One where small-scale turbulent motions dominates the physics of the system, which largest size is found to be around 1 kpc (S´anchez et al. 2010 for M33), and it corresponds with the giant stellar complex found in NGC 6946. The another regime is dominated by large-scale galactic dynamics, as the spiral arms or galactic disks analyzed in the age maps.
In one scale, the gas-star super complex located in the galaxy NGC 6946 is an excellent laboratory for the study of the interaction between the massive star formation and the gas cloud where the stars originated. The presence of a young and massive super star cluster (∼106 M, 20 Myr) appears to be the main source of ionizing photons able to generate an Hα bubble, or shell in expansion, near 730 pc in diameter.
We present for the first time the large-scale velocity field of both the approaching and receding walls of a bubble in expansion, which shows a complicated pattern with sub- bubbles forming at the walls of the largest structure, in a similar scheme to the formation ’a la Huygens’, where some points on the surface of the previous bubble become the formation center of the second-generation bubbles. Moreover, it is found supports for the hypothesis of the bubble origin due to a sequence of supernova explosions, which would provide enough energy for such a large bubble. From diagnostic diagrams is deduced that most of the ionization arises from energetic photons from massive stars, although with some locations ionized by low-velocity shocks from stellar winds and/or supernova explosions, consistent with the supersonic velocity dispersion measured throughout the shell.
In a larger scale regime, for the spirals NGC 278, NGC 1058, NGC 2500 and UGC 3574, it is studied the systematic deviations above and below the galactic plane, or corrugations, of the velocity field showing characteristic sinus-wave like patterns.
Their existence have been already reported, (e.g. Alfaro et al. 2001, Matthews & Uson 2008), but we firstly report a systematic study on the velocity corrugations in a sample of nearly face-on spiral galaxies. Corrugations are closely link, as cause/effect, to the large scale star formation processes: density waves, tidal interactions, galactic bores, collisions of high velocity clouds with disk, etc. As this part of the thesis work is still in progress, no conclusions are obtained yet about their origin or mechanisms related with the disk corrugations. This is still an open problem.
The immediate work will be finishing this analysis, as studying the correlation between the Hαemission andVZ peaks. It is interesting also determine whether there is a relation between their location in the DD and the kinematical behaviour found.
As a future plan would be also to get better spectroscopic data, since the spectra of NGC 2500 and UGC 3574 were not good enough for a more complete analysis. The Hαemission line wasn’t fitted along the slits due to the low and faint emission in some regions. Other galaxies could be included as well. Moreover PPAK data for NGC 2500 is available, and certainly it will give additional and more complete kinematical information.
At large scales also, a sample of nearby, nearly face-on, spiral galaxies, with different morphological types, has been studied kinematical and photometrically, getting the star formation information both in local and large scales.
The calculation of age maps for nearby and nearly face on spiral galaxies gives some clues in the study of the star formation and the general validity of universal star formation laws. The star formation both at local and large scales can be visualized, as this method, based on the interaction of stars and gas as a probe of star formation mechanisms, helps understanding the star formation processes and its propagation.
These 2D age maps are obtained from the comparison between the UV and Hαemission in nearby and spatially resolved galaxies, providing a global view of the star formation processes taking place in the galactic disks, the maximum scale of coherent star formation, its relation with other large scale processes of star formation in galaxies, such as density waves and, as it is the case for M51, mapping the relationship between star formation and dynamics.
This work shows that a comparison of the Hαand FUV observations of nearby spiral galaxies is a relatively direct way to probe burst age variations in spirals, which flux ratio provides a robust measure of relative age across the disk which we discuss in terms of the large-scale dynamical motions
The use of a pixel-wise age dating technique allows age mapping of the youngest stellar population present at each pixel, without any a priori assumptions about the spatial distribution of the star forming regions. The technique allows the spatial characterization of the age distribution for HII regions within a range of distance in the Local Volume, that provide enough spatial resolution to infer the internal SFH processes.
The nature or origin and persistence of spiral structure in galaxies is still an open question. There are a few main theories. The spiral structure may be tidally driven by a companion, a central rotating bar, or orbiting dark matter clumps (e.g. Bottema 2003;
Dubinski et al. 2008). A different possibility is that spirals are self-excited, where two different mechanisms are proposed. Spiral features could be the result of quasi-steady global modes of the underlying disk (Lin & Shu 1964; Bertin & Lin 1996), or they are short-lived, recurrent transient patterns from self-gravitational instabilities (Toomre 1964, 1990; Sellwood & Carlberg 1984).
The aim of this work is not to deal with such question, and the available data do not seem to be able to answer it. However, recently Dobbs & Pringle (2010), by means of numerical simulations of gas flow in spiral galaxy models, predict different distribution of
clusters of different ages depending on the mechanism for the excitation and maintenance of the spiral arms (see their Fig. 2). They suggest methods for age-dating clusters in nearby galaxies as observational tests to distinguish between the various theoretical models for spiral arm formation.
They only consider four canonical galaxy models, a fixed pattern speed, a barred galaxy, a flocculent spiral and a tidally induced spiral. And then, for each model, they estimate the current positions of star clusters of a variety of ages, ranging from∼2 Myr to around 130 Myr.
Although our data only cover the most recent SF, up to 10 Myr, these ongoing star forming regions show some similarity in their spatial distribution with the wider ranging age distribution estimated by Dobbs & Pringle (2010).
M74, M100 and M51 show the expected distribution for a spiral with a fixed pattern speed and/or a bar, a monotonic sequence of ages across the spiral arms from youngest to oldest. Although M51 was modeled as a tidally induced spiral, undergoing a double interaction, and its age distribution, from∼2Myr to around 130 Myr, do not show a clear trend. Rather a complex distribution, where clusters of different ages appear simultaneously in the same region. We obtain that the youngest stellar population does show the age gradients across the spiral arms, as predicted by standard density wave theory.
M63, with a peculiar somewhat bipolar east-west age distribution, seems to be also in agreement with the predictions by Dobbs & Pringle (2010). Where flocculent galaxies are expected to show individual spiral arm segments of similar age.
The age map for IC 2574 only shows local regions, like the giant northeastern HIIcomplex. Similarly for M94, the inner ring of intense active SF dominates its age map. Due to the lack of a global vision, any clue about the possible mechanism for the excitation of these galaxies could be obtained. The method we propose and develop here will be a powerful tool when applied to a larger sample with better signal to noise data sets.
For a future work we are interested in applying this technique to a larger number of galaxies, getting therefore the required Hα, FUV (GALEX) and TIR (SPITZER) images.
Moreover, thanks to the high amount of points obtained from the pixel-by-pixel technique, good statistics can be done getting our own IRX-βrelationship (e.g. Heckman et
al. 1995; Meurer et al. 1999). Statistical techniques applied for these purposes are simple linear and non-linear regressions between the IRX and beta variables, and to discriminate among several statistical models criteria. The Akaike’s Information Criterion was selected to compare between the linear and non-linear fits as the chosen model for the IRX-β relationship.
The techniques developed for this work have been long-slit spectroscopy and imaging, specifically optical tunable filter data, and ultraviolet and infrared archival images. The reduction of long-slit spectra as well as tunable filter images have been required, using the IRAF techniques for these purposes.